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Artificial intelligence (AI) tools offer unprecedented opportunities to streamline workflows and enhance decision-making. From automating invoice processing to generating real-time financial insights, AI is in fact revolutionizing work in finance and accounting. Yet, despite the clear benefits, many finance teams find themselves stuck at the starting line.
Most CFOs and finance leaders recognize AI's potential. However, four critical challenges consistently emerge as the primary barriers preventing successful AI adoption in finance departments. Let's dive into these obstacles and explore practical strategies to overcome them.
Most finance teams are drowning in information that's scattered across multiple systems, stored in various formats, and often inconsistent or incomplete.
Think about your typical finance operation. Purchase orders live in one system, contracts and bookings data live in another, and headcount and compensation in a third. Meanwhile, budget data sits in spreadsheets, contracts are stored as PDFs in shared drives, and vendor information exists across multiple databases with different formatting standards.
This data fragmentation creates several problems:
• Inconsistent data formats make it difficult for AI systems to process information effectively
• Missing connections between related data points prevent comprehensive analysis
• Manual data preparation consumes significant time and resources before any AI implementation
The most applicable AI tools for FP&A and accounting workflows overcome this with direct transaction-level integrations to source systems like your general ledger. This unlocks your data securely for AI agents to analyze, interpret and provide necessary context as it completes tasks.
The solution starts with data consolidation and standardization. Begin by mapping your current data landscape. Where does your financial information live? How is it structured? What are the key relationships between different data sources?
Consider how you utilize a data warehouse or modern FP&A system that can serve as a central repository and source of truth for reporting and planning. Are you reliant on extract files from your HR and CRM system for information like contract details and sales compensation plans?
Centralized AI platforms integrate to source systems like your general ledger, while enabling secure file upload capabiltiies, so you don't have to manually aggregate and clean all of this data to analyze for business partners.
If you tried to hire a qualified accounting and finance professional recently, it likely took you several months as these skills are becoming increasingly difficult to find and retain. The talent shortage isn't just about quantity—it's about finding people with the right mix of traditional accounting and finance skills, and technology acumen.
A recent AICPA survey revealed that the number of qualified candidates for accounting roles fell by 32% in 2016. Other surveys indicate that over 70% of finance leaders struggle to fill open positions. When they do find candidates, many lack the technical skills needed to work effectively with AI tools and data analytics platforms.
This talent gap creates a vicious cycle. Finance teams are understaffed and overwhelmed with routine tasks, leaving little time to develop strategic internal partnerships to add greater value.
The key is developing your existing talent while strategically adding new capabilities sourced from external advisors and partners. Consider these approaches:
• Cross-training programs that help traditional accountants develop data analysis skills
• Partnership with IT teams to bridge the technical gap during AI implementation
• Outsourcing routine tasks to free up internal resources for higher-value activities
• Hiring for potential rather than just current skills, focusing on adaptability and willingness to learn
Many successful finance teams are also creating hybrid roles that combine traditional finance responsibilities with technology management. These "finance technologists" become internal champions who can translate between technical capabilities and business needs.
Budget constraints represent perhaps the most universal challenge facing finance teams today. Executives expect finance departments to control costs while simultaneously investing in transformation initiatives. It's a delicate balancing act that often leaves AI projects underfunded or indefinitely postponed.
The perception that AI implementation requires massive upfront investments doesn't help. Many finance leaders envision costly enterprise software licenses, extensive consulting engagements, and months of system integration work.
Cloud-based solutions now provide pre-built finance-specific AI capabilities that don’t require implementations.
High-performance finance teams are taking a strategic approach to AI investment which starts with assessing and identifying where the most value lies.
Start small and prove value. Choose one specific process—like expense report processing or vendor onboarding—where AI can deliver quick, measurable results. Use those early wins to justify additional investment.
Consider AI-powered services rather than software. Some organizations find better value in outsourcing specific functions to providers who already have AI capabilities built into their service delivery.
Focus on high-volume, repetitive tasks. These typically offer the fastest payback on AI investments while freeing up staff time for more strategic work.
Your finance department probably relies on a complex ecosystem of software applications. ERP systems, CRM platforms, banking software, expense management tools, and specialized accounting applications all need to work together seamlessly for AI to be effective.
Unfortunately, many of these systems weren't designed to integrate easily. Legacy applications may lack modern APIs. Different vendors use incompatible data formats. Some systems require manual exports and imports that break the real-time data flow AI depends on.
These integration challenges can turn a promising project into a technical nightmare. Teams get bogged down in system connectivity issues rather than focusing on the business value they hoped to achieve.
Successful AI adoption often requires a thoughtful approach to system architecture and one that our team and solutions are equipped to solve.Audit your current technology stack to identify integration capabilities and limitations. Document what systems need to communicate with each other and how data currently flows between them.
Invest in integrated FP&A platforms that connect your various accounting, CRM, HR, and other systems. When evaluating your options, be sure to consider the implementation process and expertise. Prioritize API-first solutions when evaluating new software purchases. Applications with robust APIs will integrate more easily with AI tools and other systems.
Consider gradual system modernization rather than trying to connect everything at once. Sometimes replacing one legacy system that's blocking integration can unlock AI opportunities across multiple processes.
The onset of generative AI brought many great productivity benefits and saw the quickest adoption of any technology, ever.
AI technology and capabilities quickly evolved with the advent of a Agentic AI—with agents that can work autonomously to complete complex, multi-step tasks across different systems—is already transforming specific areas of finance operations.
But the real value goes beyond efficiency. By automating routine tasks, finance teams can focus on relationship management, cash flow optimization, and forecasting that drive real business value. A human remains in the loop and at the controls of the AI agents, unlocking your data, freeing your capacity and putting you in the driver seat of strategy and decision-making support.
Assessing and implementing AI in finance doesn't have to be overwhelming. While these four challenges are real and significant, they're not insurmountable. Here are the essential steps to move forward:
Address data quality first. AI adoption is most useful with clean, accessible, and well-structured data. Make this your foundation.
Research and test. Consider external expertise and advisory service to scope out use cases. Demo new products and build an internal AI champion who can drive adoption.
Think strategically about resource allocation. Focus on high-impact opportunities that can demonstrate value and significant savings.
Plan for integration from the beginning. Understanding your system landscape and connectivity requirements early will prevent delays and frustrations later.
The finance teams that successfully implement AI aren't necessarily the ones with the biggest budgets or the most technical expertise. They're the ones that acknowledge these challenges upfront and develop practical strategies to address them systematically.
AI in finance is a rapidly evolving journey. By tackling these four fundamental challenges, you'll build the foundation needed for long-term success in leveraging artificial intelligence to transform your finance operations. Small, focused steps often lead to the greatest value.
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